calc_beta_topic_labels: Computes beta topic labels

In clintpgeorge/ldamcmc: Markov chain Monte Carlo Algorithms for the Latent Dirichlet Allocation Model

Description

Computes topic lables for each β_j^(s), s = 1, …, S sampled from the FGS or ACGS chain

Usage

calc_beta_topic_labels(true.beta, mc.betas)

Arguments

true.beta	the true β (a K \times V matrix) computed from the corpus using true document topic labels and calc_class_term_frequency, where K represents the number of topics in the corpus and V represents the number of terms in the vocabulary.
mc.betas	a K \times V \times S matrix, which has the β^(s), s = 1, …, S samples from a FGS or ACGS chain

Value

A K \times S matrix of β_j^(s) topic labels

See Also

calc_class_term_frequency, normalize

clintpgeorge/ldamcmc documentation built on Feb. 22, 2020, 12:39 p.m.